Next, we compared M2A's performance to other SOTA temporal mechanisms including, TSM [1]}, TEA [2]}, TDN [3]}, and TAM [4]}. TSM doesn't incorporate any attention or motion information and instead shifts values across consecutive frames, while TEA and TDN are motion-only temporal mechanisms, and TAM is an attention-only temporal mechanism. The TDN and TEA papers involve multiple mechanisms, some of which modify the backbone directly, so to conduct a fair comparison, we only use the individual temporal mechanisms of each method. Specifically, we use the long-term mechanism for TDN and the motion excitation mechanism for TEA. In all our experiments we use the respective paper's publicly available code. Furthermore, to understand if M2A can further improve the performance of other temporal mechanisms, we also compare using both M2A and TSM (M2A + TSM). Table REF  shows that M2A can achieve higher accuracy than complex state-of-the-art motion/attention-only mechanisms (TEA, TDN, and TAM). Furthermore, we see that M2A is comparable in terms of GMACs/Video to all the other mechanisms which shows that it is a computationally efficient mechanism. Comparing M2A to TSM, we see that TSM outperforms M2A in top-1 accuracy by approximately 4%. However, we also see that M2A + TSM outperforms TSM by +0.3% in top-1 accuracy, showing that M2A is a complementary mechanism that can be combined with other temporal mechanisms to achieve better performance.
